Exact(43)
Vine category proportion data were compared to the measured vine proportion data to validate the basic assumption supporting these calculations.
Table 1 reports the best fit obtained for the Wetmore et al. (2015) response proportion data, and Fig. 5 shows the empirical data and best-fitting WITNESS ROCs.
For the secondary industry, we apply the proportion data during 2008 2014 which has minor variation and is in the closest period to 2020.
Several statistical regression models to manage continuous proportion data are compared, these being: Generalized linear models (GLM) with Binomial, Poisson and Gamma errors after several transformations of the data and Beta regression on the raw data.
The habitat preferences of these species were assessed using abundance and proportion data from 71 independent sites sampled using pitfall traps over 2 years and a selection of repeat first-year sites sampled during the second year, incorporating a range of land-uses from extensive moorland, through grasslands to intensive arable fields.
Proportion data were arcsin-square root transformed to improve symmetry [49].
Similar(16)
For both the response and the explanatory variable arcsine square root transformed values were used, because the original variables were proportion data-bound at the extremes.
For the secondary endpoint (proportions), data were compared using the Chi-Squared test.
Fisher's exact test was used to compare proportions data.
The model required epidemiological, resource use, unit cost, and market share proportions data.
Estimating the actual proportion of data from the input data stream could allow to dynamically adapt ensembles to reflect operational conditions.
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